import os, sys
import pickle, zstd
import numpy as np
import time
import torch

from inferemote.atlas_applet import AtlasApplet
from inferemote.image_encoder import ImageEncoder
from torchvision.ops import nms

conf_thres=0.25
iou_thres=0.45

class Applet(AtlasApplet):
    MODEL_WIDTH=640
    MODEL_HEIGHT=640

    def __init__(self):
        # self.orig_shape = orig_shape
        super().__init__(port=5600)
    
    def pre_process(self,blob):
        image = ImageEncoder.bytes_to_image(blob)
        image = image.astype(np.float32)
        image = image / 255
        '''Transposing must go here, after jpeg decoding by cv2'''
        image = image.transpose(2, 0, 1).tobytes()

        return image

    def post_process(self,result_list):
        pred = np.frombuffer(bytearray(result_list[0]), dtype=np.float32)
        pred = pred.reshape(1, 25200, -1)
        pred = torch.tensor(pred)
        pred = self.non_max_suppression(pred, conf_thres, iou_thres, None, False, max_det=1000)
        blob = pickle.dumps(pred)       
        #blob = zstd.compress(blob, 1)

        return [blob]
    
    def non_max_suppression(self,prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
                        labels=(), max_det=300):
        """Runs Non-Maximum Suppression (NMS) on inference results

        Returns:
            list of detections, on (n,6) tensor per image [xyxy, conf, cls]
        """

        nc = prediction.shape[2] - 5  # number of classes
        xc = prediction[..., 4] > conf_thres  # candidates

        # Checks
        assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
        assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'

        # Settings
        min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
        max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
        time_limit = 10.0  # seconds to quit after
        redundant = True  # require redundant detections
        multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
        merge = False  # use merge-NMS

        t = time.time()
        output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
        for xi, x in enumerate(prediction):  # image index, image inference
            # Apply constraints
            # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
            x = x[xc[xi]]  # confidence

            # Cat apriori labels if autolabelling
            if labels and len(labels[xi]):
                l = labels[xi]
                v = torch.zeros((len(l), nc + 5), device=x.device)
                v[:, :4] = l[:, 1:5]  # box
                v[:, 4] = 1.0  # conf
                v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
                x = torch.cat((x, v), 0)

            # If none remain process next image
            if not x.shape[0]:
                continue

            # Compute conf
            x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

            # Box (center x, center y, width, height) to (x1, y1, x2, y2)
            box = self.xywh2xyxy(x[:, :4])

            # Detections matrix nx6 (xyxy, conf, cls)
            if multi_label:
                i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
                x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
            else:  # best class only
                conf, j = x[:, 5:].max(1, keepdim=True)
                x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]

            # Filter by class
            if classes is not None:
                x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

            # Apply finite constraint
            # if not torch.isfinite(x).all():
            #     x = x[torch.isfinite(x).all(1)]

            # Check shape
            n = x.shape[0]  # number of boxes
            if not n:  # no boxes
                continue
            elif n > max_nms:  # excess boxes
                x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence

            # Batched NMS
            c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
            boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
            i = nms(boxes, scores, iou_thres)  # NMS
            if i.shape[0] > max_det:  # limit detections
                i = i[:max_det]
            if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
                # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
                iou = self.box_iou(boxes[i], boxes) > iou_thres  # iou matrix
                weights = iou * scores[None]  # box weights
                x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
                if redundant:
                    i = i[iou.sum(1) > 1]  # require redundancy

            output[xi] = x[i]
            if (time.time() - t) > time_limit:
                print(f'WARNING: NMS time limit {time_limit}s exceeded')
                break  # time limit exceeded

        return output

    def xywh2xyxy(self,x):
        # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
        y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
        y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
        y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
        y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
        y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
        return y
    
    def box_iou(self,box1, box2):
        # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
        """
        Return intersection-over-union (Jaccard index) of boxes.
        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
        Arguments:
            box1 (Tensor[N, 4])
            box2 (Tensor[M, 4])
        Returns:
            iou (Tensor[N, M]): the NxM matrix containing the pairwise
                IoU values for every element in boxes1 and boxes2
        """

        def box_area(box):
            # box = 4xn
            return (box[2] - box[0]) * (box[3] - box[1])

        area1 = box_area(box1.T)
        area2 = box_area(box2.T)

        # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
        inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
        return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)    



if __name__ == '__main__':
    '''Set the original shape of images from you applications here.'''
    #orig_shape = (720, 1280, 3)
    Applet().run()
